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Record W7030372476

Muscle and plasma protein synthesis in response to a leucine-enriched meal in sarco-dynapenic older women

2014· dissertation· en· W7030372476 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2014
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsMcGill University
Fundersnot available
KeywordsLeucineMyofibrilAnabolismSarcoplasmProtein turnoverProtein biosynthesisProtein catabolismAmino acidSkeletal muscle
DOInot available

Abstract

fetched live from OpenAlex

Sarcopenia (muscle loss) and dynapenia (loss of strength)are risk factors for disabilities, falls and loss of autonomy with aging.The essential amino acid leucine is asignaling molecule that stimulates mRNA translation, protein synthesis and insulin secretion, making it a potential anabolic agent.This thesis assessed the effect of leucine added to a meal on whole-body leucine balance, myofibrillar and sarcoplasmic proteins and on total plasma protein fractional synthesis rates (FSR) in 13 older women.The design was a randomized, double-blind crossover versus an isonitrogenous meal.A possible effect of sarco-dynapenia on these variables was tested (n=7 with sarco-dynapenia, n=6 controls).Greater whole-body leucine balance (intake -oxidation, measured by dilution of stable isotope [1-13 C]leucine),after the meal+leucine was contributed by enhanced myofibrillar protein synthesis, measured by incorporation of [ 2 H 5 ]phenylalanine.Sarcoplasmic and total plasma protein FSR wereequally stimulated by both meals.There was no effect of sarco-dynapenia on any of these protein kinetic responses.Data suggest that additional leucine stimulates myofibrillar protein synthesis and greater total retention.The anabolic capacity was maintained in women with sarco-dynapenia who could benefit from such an intervention.Future studies will determine whether long-term leucine supplementation translates into improved muscle mass, strength and function.Above all, I would like to extend significant gratitude to my supervisor, Dr. Stphanie Chevalier, who has been a true inspiration throughout my journey, on both an academic and personal level.Words cannot express how deeply thankful and honoured I am to have been able to learn from her.Her understanding, patience and constant guidance have been instrumental throughout my thesis, always leading me to higher grounds

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.215
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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